Overview

Brought to you by YData

Dataset statistics

Number of variables17
Number of observations206068
Missing cells6291
Missing cells (%)0.2%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory170.8 MiB
Average record size in memory869.3 B

Variable types

Numeric2
Categorical6
Text9

Alerts

AÑO is highly overall correlated with PERIODOHigh correlation
CICLO DE CULTIVO is highly overall correlated with ESTADO FISICO PRODUCCION and 2 other fieldsHigh correlation
CÓD. DEP. is highly overall correlated with DEPARTAMENTOHigh correlation
DEPARTAMENTO is highly overall correlated with CÓD. DEP.High correlation
ESTADO FISICO PRODUCCION is highly overall correlated with CICLO DE CULTIVO and 1 other fieldsHigh correlation
GRUPO DE CULTIVO is highly overall correlated with CICLO DE CULTIVO and 1 other fieldsHigh correlation
PERIODO is highly overall correlated with AÑO and 1 other fieldsHigh correlation
Rendimiento (t/ha) has 3433 (1.7%) missing valuesMissing
NOMBRE CIENTIFICO has 2857 (1.4%) missing valuesMissing

Reproduction

Analysis started2025-11-11 02:16:04.108487
Analysis finished2025-11-11 02:16:13.406804
Duration9.3 seconds
Software versionydata-profiling vv4.17.0
Download configurationconfig.json

Variables

CÓD. DEP.
Real number (ℝ)

High correlation 

Distinct32
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean40.322563
Minimum5
Maximum99
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.6 MiB
2025-11-10T21:16:13.473106image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum5
5-th percentile5
Q117
median41
Q368
95-th percentile76
Maximum99
Range94
Interquartile range (IQR)51

Descriptive statistics

Standard deviation25.278662
Coefficient of variation (CV)0.62691108
Kurtosis-1.2790107
Mean40.322563
Median Absolute Deviation (MAD)24
Skewness0.23041379
Sum8309190
Variance639.01074
MonotonicityNot monotonic
2025-11-10T21:16:13.578929image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=32)
ValueCountFrequency (%)
1520576
 
10.0%
518759
 
9.1%
2517805
 
8.6%
4115926
 
7.7%
7615774
 
7.7%
6814672
 
7.1%
5213445
 
6.5%
549751
 
4.7%
738595
 
4.2%
198385
 
4.1%
Other values (22)62380
30.3%
ValueCountFrequency (%)
518759
9.1%
83809
 
1.8%
135068
 
2.5%
1520576
10.0%
175342
 
2.6%
182344
 
1.1%
198385
4.1%
204887
 
2.4%
235121
 
2.5%
2517805
8.6%
ValueCountFrequency (%)
99668
 
0.3%
97275
 
0.1%
95487
 
0.2%
94162
 
0.1%
91567
 
0.3%
88138
 
0.1%
861776
 
0.9%
853142
 
1.5%
81857
 
0.4%
7615774
7.7%

DEPARTAMENTO
Categorical

High correlation 

Distinct32
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size11.6 MiB
BOYACA
20576 
ANTIOQUIA
18759 
CUNDINAMARCA
17805 
HUILA
15926 
VALLE DEL CAUCA
15774 
Other values (27)
117228 

Length

Max length24
Median length15
Mean length8.398582
Min length4

Characters and Unicode

Total characters1730679
Distinct characters24
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowBOYACA
2nd rowCUNDINAMARCA
3rd rowCUNDINAMARCA
4th rowNORTE DE SANTANDER
5th rowNORTE DE SANTANDER

Common Values

ValueCountFrequency (%)
BOYACA20576
 
10.0%
ANTIOQUIA18759
 
9.1%
CUNDINAMARCA17805
 
8.6%
HUILA15926
 
7.7%
VALLE DEL CAUCA15774
 
7.7%
SANTANDER14672
 
7.1%
NARIÑO13445
 
6.5%
NORTE DE SANTANDER9751
 
4.7%
TOLIMA8595
 
4.2%
CAUCA8385
 
4.1%
Other values (22)62380
30.3%

Length

2025-11-10T21:16:13.679688image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
santander24423
 
9.4%
cauca24159
 
9.3%
boyaca20576
 
7.9%
antioquia18759
 
7.2%
cundinamarca17805
 
6.8%
huila15926
 
6.1%
del15774
 
6.0%
valle15774
 
6.0%
nariño13445
 
5.2%
norte9751
 
3.7%
Other values (28)84426
32.4%

Most occurring characters

ValueCountFrequency (%)
A375105
21.7%
C143467
 
8.3%
N142326
 
8.2%
I117843
 
6.8%
O104791
 
6.1%
E100536
 
5.8%
R98671
 
5.7%
L96066
 
5.6%
U96028
 
5.5%
D89791
 
5.2%
Other values (14)366055
21.2%

Most occurring categories

ValueCountFrequency (%)
(unknown)1730679
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
A375105
21.7%
C143467
 
8.3%
N142326
 
8.2%
I117843
 
6.8%
O104791
 
6.1%
E100536
 
5.8%
R98671
 
5.7%
L96066
 
5.6%
U96028
 
5.5%
D89791
 
5.2%
Other values (14)366055
21.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown)1730679
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
A375105
21.7%
C143467
 
8.3%
N142326
 
8.2%
I117843
 
6.8%
O104791
 
6.1%
E100536
 
5.8%
R98671
 
5.7%
L96066
 
5.6%
U96028
 
5.5%
D89791
 
5.2%
Other values (14)366055
21.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown)1730679
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
A375105
21.7%
C143467
 
8.3%
N142326
 
8.2%
I117843
 
6.8%
O104791
 
6.1%
E100536
 
5.8%
R98671
 
5.7%
L96066
 
5.6%
U96028
 
5.5%
D89791
 
5.2%
Other values (14)366055
21.2%
Distinct1105
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size10.8 MiB
2025-11-10T21:16:13.960341image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length6
Median length6
Mean length5.8904828
Min length5

Characters and Unicode

Total characters1213840
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row15,114
2nd row25,754
3rd row25,214
4th row54,405
5th row54,518
ValueCountFrequency (%)
76,111655
 
0.3%
41,298639
 
0.3%
76,892634
 
0.3%
76,834627
 
0.3%
41,306623
 
0.3%
41,001602
 
0.3%
76,622602
 
0.3%
5,001595
 
0.3%
41,396591
 
0.3%
41,020590
 
0.3%
Other values (1095)199910
97.0%
2025-11-10T21:16:14.291312image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
,206068
17.0%
5151048
12.4%
1124805
10.3%
2110018
9.1%
0109609
9.0%
6102341
8.4%
7101631
8.4%
494068
7.7%
887347
7.2%
380821
 
6.7%

Most occurring categories

ValueCountFrequency (%)
(unknown)1213840
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
,206068
17.0%
5151048
12.4%
1124805
10.3%
2110018
9.1%
0109609
9.0%
6102341
8.4%
7101631
8.4%
494068
7.7%
887347
7.2%
380821
 
6.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown)1213840
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
,206068
17.0%
5151048
12.4%
1124805
10.3%
2110018
9.1%
0109609
9.0%
6102341
8.4%
7101631
8.4%
494068
7.7%
887347
7.2%
380821
 
6.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown)1213840
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
,206068
17.0%
5151048
12.4%
1124805
10.3%
2110018
9.1%
0109609
9.0%
6102341
8.4%
7101631
8.4%
494068
7.7%
887347
7.2%
380821
 
6.7%
Distinct1018
Distinct (%)0.5%
Missing1
Missing (%)< 0.1%
Memory size11.4 MiB
2025-11-10T21:16:14.483977image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length27
Median length22
Mean length8.5076019
Min length3

Characters and Unicode

Total characters1753136
Distinct characters29
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowBUSBANZA
2nd rowSOACHA
3rd rowCOTA
4th rowLOS PATIOS
5th rowPAMPLONA
ValueCountFrequency (%)
san14268
 
5.1%
la9093
 
3.3%
de8975
 
3.2%
el7644
 
2.8%
santa3629
 
1.3%
del3482
 
1.3%
puerto3457
 
1.2%
juan1669
 
0.6%
carmen1594
 
0.6%
pedro1285
 
0.5%
Other values (1006)222864
80.2%
2025-11-10T21:16:14.768683image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
A327612
18.7%
O134887
 
7.7%
E127932
 
7.3%
I127198
 
7.3%
R117761
 
6.7%
N108420
 
6.2%
L105968
 
6.0%
S81982
 
4.7%
C81587
 
4.7%
T77185
 
4.4%
Other values (19)462604
26.4%

Most occurring categories

ValueCountFrequency (%)
(unknown)1753136
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
A327612
18.7%
O134887
 
7.7%
E127932
 
7.3%
I127198
 
7.3%
R117761
 
6.7%
N108420
 
6.2%
L105968
 
6.0%
S81982
 
4.7%
C81587
 
4.7%
T77185
 
4.4%
Other values (19)462604
26.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown)1753136
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
A327612
18.7%
O134887
 
7.7%
E127932
 
7.3%
I127198
 
7.3%
R117761
 
6.7%
N108420
 
6.2%
L105968
 
6.0%
S81982
 
4.7%
C81587
 
4.7%
T77185
 
4.4%
Other values (19)462604
26.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown)1753136
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
A327612
18.7%
O134887
 
7.7%
E127932
 
7.3%
I127198
 
7.3%
R117761
 
6.7%
N108420
 
6.2%
L105968
 
6.0%
S81982
 
4.7%
C81587
 
4.7%
T77185
 
4.4%
Other values (19)462604
26.4%

GRUPO DE CULTIVO
Categorical

High correlation 

Distinct13
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size12.0 MiB
FRUTALES
50236 
CEREALES
36045 
HORTALIZAS
32032 
TUBERCULOS Y PLATANOS
30664 
LEGUMINOSAS
26368 
Other values (8)
30723 

Length

Max length48
Median length21
Mean length12.02059
Min length6

Characters and Unicode

Total characters2477059
Distinct characters23
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowHORTALIZAS
2nd rowHORTALIZAS
3rd rowHORTALIZAS
4th rowHORTALIZAS
5th rowHORTALIZAS

Common Values

ValueCountFrequency (%)
FRUTALES50236
24.4%
CEREALES36045
17.5%
HORTALIZAS32032
15.5%
TUBERCULOS Y PLATANOS30664
14.9%
LEGUMINOSAS26368
12.8%
OTROS PERMANENTES21813
10.6%
FIBRAS1977
 
1.0%
OLEAGINOSAS1967
 
1.0%
PLANTAS AROMATICAS, CONDIMENTARIAS Y MEDICINALES1686
 
0.8%
FORESTALES1327
 
0.6%
Other values (3)1953
 
0.9%

Length

2025-11-10T21:16:14.848858image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
frutales50236
16.8%
cereales36045
12.1%
y33331
11.2%
hortalizas32032
10.7%
tuberculos30664
10.3%
platanos30664
10.3%
leguminosas26368
8.8%
otros22769
7.6%
permanentes21813
7.3%
fibras1977
 
0.7%
Other values (10)12972
 
4.3%

Most occurring characters

ValueCountFrequency (%)
S296158
12.0%
E292483
11.8%
A282517
11.4%
L215618
8.7%
R203128
8.2%
T196475
7.9%
O177805
 
7.2%
U137932
 
5.6%
N110341
 
4.5%
92803
 
3.7%
Other values (13)471799
19.0%

Most occurring categories

ValueCountFrequency (%)
(unknown)2477059
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
S296158
12.0%
E292483
11.8%
A282517
11.4%
L215618
8.7%
R203128
8.2%
T196475
7.9%
O177805
 
7.2%
U137932
 
5.6%
N110341
 
4.5%
92803
 
3.7%
Other values (13)471799
19.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown)2477059
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
S296158
12.0%
E292483
11.8%
A282517
11.4%
L215618
8.7%
R203128
8.2%
T196475
7.9%
O177805
 
7.2%
U137932
 
5.6%
N110341
 
4.5%
92803
 
3.7%
Other values (13)471799
19.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown)2477059
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
S296158
12.0%
E292483
11.8%
A282517
11.4%
L215618
8.7%
R203128
8.2%
T196475
7.9%
O177805
 
7.2%
U137932
 
5.6%
N110341
 
4.5%
92803
 
3.7%
Other values (13)471799
19.0%
Distinct120
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size11.1 MiB
2025-11-10T21:16:15.019149image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length32
Median length22
Mean length6.3588233
Min length3

Characters and Unicode

Total characters1310350
Distinct characters26
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3 ?
Unique (%)< 0.1%

Sample

1st rowACELGA
2nd rowACELGA
3rd rowACELGA
4th rowACELGA
5th rowACELGA
ValueCountFrequency (%)
maiz25199
 
11.3%
frijol14693
 
6.6%
tomate12381
 
5.5%
yuca9488
 
4.3%
platano9048
 
4.1%
caña8118
 
3.6%
citricos7781
 
3.5%
papa7483
 
3.4%
arroz7416
 
3.3%
cafe7263
 
3.3%
Other values (115)114234
51.2%
2025-11-10T21:16:15.281223image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
A279809
21.4%
O116972
 
8.9%
I94522
 
7.2%
C93527
 
7.1%
R77846
 
5.9%
L75964
 
5.8%
E69867
 
5.3%
T69852
 
5.3%
M57435
 
4.4%
P42668
 
3.3%
Other values (16)331888
25.3%

Most occurring categories

ValueCountFrequency (%)
(unknown)1310350
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
A279809
21.4%
O116972
 
8.9%
I94522
 
7.2%
C93527
 
7.1%
R77846
 
5.9%
L75964
 
5.8%
E69867
 
5.3%
T69852
 
5.3%
M57435
 
4.4%
P42668
 
3.3%
Other values (16)331888
25.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown)1310350
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
A279809
21.4%
O116972
 
8.9%
I94522
 
7.2%
C93527
 
7.1%
R77846
 
5.9%
L75964
 
5.8%
E69867
 
5.3%
T69852
 
5.3%
M57435
 
4.4%
P42668
 
3.3%
Other values (16)331888
25.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown)1310350
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
A279809
21.4%
O116972
 
8.9%
I94522
 
7.2%
C93527
 
7.1%
R77846
 
5.9%
L75964
 
5.8%
E69867
 
5.3%
T69852
 
5.3%
M57435
 
4.4%
P42668
 
3.3%
Other values (16)331888
25.3%

CULTIVO
Text

Distinct223
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size11.3 MiB
2025-11-10T21:16:15.473382image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length32
Median length18
Mean length6.6554002
Min length2

Characters and Unicode

Total characters1371465
Distinct characters25
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique9 ?
Unique (%)< 0.1%

Sample

1st rowACELGA
2nd rowACELGA
3rd rowACELGA
4th rowACELGA
5th rowACELGA
ValueCountFrequency (%)
maiz25199
 
10.6%
frijol14693
 
6.2%
tomate12381
 
5.2%
yuca9488
 
4.0%
platano9048
 
3.8%
de8230
 
3.5%
caña8118
 
3.4%
papa7483
 
3.2%
arroz7416
 
3.1%
cafe7263
 
3.1%
Other values (230)128086
54.0%
2025-11-10T21:16:15.772681image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
A308653
22.5%
O114915
 
8.4%
R87590
 
6.4%
I83541
 
6.1%
L82168
 
6.0%
E80969
 
5.9%
C79683
 
5.8%
M64448
 
4.7%
T57127
 
4.2%
N54170
 
3.9%
Other values (15)358201
26.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)1371465
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
A308653
22.5%
O114915
 
8.4%
R87590
 
6.4%
I83541
 
6.1%
L82168
 
6.0%
E80969
 
5.9%
C79683
 
5.8%
M64448
 
4.7%
T57127
 
4.2%
N54170
 
3.9%
Other values (15)358201
26.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)1371465
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
A308653
22.5%
O114915
 
8.4%
R87590
 
6.4%
I83541
 
6.1%
L82168
 
6.0%
E80969
 
5.9%
C79683
 
5.8%
M64448
 
4.7%
T57127
 
4.2%
N54170
 
3.9%
Other values (15)358201
26.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)1371465
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
A308653
22.5%
O114915
 
8.4%
R87590
 
6.4%
I83541
 
6.1%
L82168
 
6.0%
E80969
 
5.9%
C79683
 
5.8%
M64448
 
4.7%
T57127
 
4.2%
N54170
 
3.9%
Other values (15)358201
26.1%
Distinct271
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size11.7 MiB
2025-11-10T21:16:15.988547image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length32
Median length20
Mean length8.9943465
Min length2

Characters and Unicode

Total characters1853447
Distinct characters30
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique9 ?
Unique (%)< 0.1%

Sample

1st rowACELGA
2nd rowACELGA
3rd rowACELGA
4th rowACELGA
5th rowACELGA
ValueCountFrequency (%)
maiz25199
 
8.8%
tradicional20069
 
7.0%
frijol14691
 
5.2%
tomate12381
 
4.3%
yuca9488
 
3.3%
platano9007
 
3.2%
de8230
 
2.9%
caña8118
 
2.8%
papa7619
 
2.7%
arroz7416
 
2.6%
Other values (280)162856
57.1%
2025-11-10T21:16:16.286353image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
A377306
20.4%
O157143
 
8.5%
I146009
 
7.9%
R119598
 
6.5%
C118940
 
6.4%
L113083
 
6.1%
E103003
 
5.6%
N94280
 
5.1%
T85128
 
4.6%
79006
 
4.3%
Other values (20)459951
24.8%

Most occurring categories

ValueCountFrequency (%)
(unknown)1853447
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
A377306
20.4%
O157143
 
8.5%
I146009
 
7.9%
R119598
 
6.5%
C118940
 
6.4%
L113083
 
6.1%
E103003
 
5.6%
N94280
 
5.1%
T85128
 
4.6%
79006
 
4.3%
Other values (20)459951
24.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown)1853447
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
A377306
20.4%
O157143
 
8.5%
I146009
 
7.9%
R119598
 
6.5%
C118940
 
6.4%
L113083
 
6.1%
E103003
 
5.6%
N94280
 
5.1%
T85128
 
4.6%
79006
 
4.3%
Other values (20)459951
24.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown)1853447
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
A377306
20.4%
O157143
 
8.5%
I146009
 
7.9%
R119598
 
6.5%
C118940
 
6.4%
L113083
 
6.1%
E103003
 
5.6%
N94280
 
5.1%
T85128
 
4.6%
79006
 
4.3%
Other values (20)459951
24.8%

AÑO
Categorical

High correlation 

Distinct13
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size10.6 MiB
2,017
18756 
2,016
18399 
2,015
17900 
2,013
17649 
2,014
17434 
Other values (8)
115930 

Length

Max length5
Median length5
Mean length5
Min length5

Characters and Unicode

Total characters1030340
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2,006
2nd row2,006
3rd row2,006
4th row2,006
5th row2,006

Common Values

ValueCountFrequency (%)
2,01718756
9.1%
2,01618399
8.9%
2,01517900
8.7%
2,01317649
8.6%
2,01417434
8.5%
2,01216856
8.2%
2,01016619
8.1%
2,01116592
8.1%
2,00916318
7.9%
2,00815894
7.7%
Other values (3)33651
16.3%

Length

2025-11-10T21:16:16.362382image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2,01718756
9.1%
2,01618399
8.9%
2,01517900
8.7%
2,01317649
8.6%
2,01417434
8.5%
2,01216856
8.2%
2,01016619
8.1%
2,01116592
8.1%
2,00916318
7.9%
2,00815894
7.7%
Other values (3)33651
16.3%

Most occurring characters

ValueCountFrequency (%)
0274454
26.6%
2222924
21.6%
,206068
20.0%
1170893
16.6%
734239
 
3.3%
829990
 
2.9%
622471
 
2.2%
517900
 
1.7%
317649
 
1.7%
417434
 
1.7%

Most occurring categories

ValueCountFrequency (%)
(unknown)1030340
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0274454
26.6%
2222924
21.6%
,206068
20.0%
1170893
16.6%
734239
 
3.3%
829990
 
2.9%
622471
 
2.2%
517900
 
1.7%
317649
 
1.7%
417434
 
1.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown)1030340
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0274454
26.6%
2222924
21.6%
,206068
20.0%
1170893
16.6%
734239
 
3.3%
829990
 
2.9%
622471
 
2.2%
517900
 
1.7%
317649
 
1.7%
417434
 
1.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown)1030340
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0274454
26.6%
2222924
21.6%
,206068
20.0%
1170893
16.6%
734239
 
3.3%
829990
 
2.9%
622471
 
2.2%
517900
 
1.7%
317649
 
1.7%
417434
 
1.7%

PERIODO
Categorical

High correlation 

Distinct36
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size10.5 MiB
2018
 
8901
2017
 
8899
2016
 
8681
2015
 
8487
2014
 
8332
Other values (31)
162768 

Length

Max length5
Median length5
Mean length4.528675
Min length4

Characters and Unicode

Total characters933215
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2006B
2nd row2006B
3rd row2006B
4th row2006B
5th row2006B

Common Values

ValueCountFrequency (%)
20188901
 
4.3%
20178899
 
4.3%
20168681
 
4.2%
20158487
 
4.1%
20148332
 
4.0%
20138267
 
4.0%
20127962
 
3.9%
20117781
 
3.8%
20107736
 
3.8%
20097632
 
3.7%
Other values (26)123390
59.9%

Length

2025-11-10T21:16:16.442974image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
20188901
 
4.3%
20178899
 
4.3%
20168681
 
4.2%
20158487
 
4.1%
20148332
 
4.0%
20138267
 
4.0%
20127962
 
3.9%
20117781
 
3.8%
20107736
 
3.8%
20097632
 
3.7%
Other values (26)123390
59.9%

Most occurring characters

ValueCountFrequency (%)
0274454
29.4%
2222924
23.9%
1170893
18.3%
A58003
 
6.2%
B50940
 
5.5%
734239
 
3.7%
829990
 
3.2%
622471
 
2.4%
517900
 
1.9%
317649
 
1.9%
Other values (2)33752
 
3.6%

Most occurring categories

ValueCountFrequency (%)
(unknown)933215
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0274454
29.4%
2222924
23.9%
1170893
18.3%
A58003
 
6.2%
B50940
 
5.5%
734239
 
3.7%
829990
 
3.2%
622471
 
2.4%
517900
 
1.9%
317649
 
1.9%
Other values (2)33752
 
3.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown)933215
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0274454
29.4%
2222924
23.9%
1170893
18.3%
A58003
 
6.2%
B50940
 
5.5%
734239
 
3.7%
829990
 
3.2%
622471
 
2.4%
517900
 
1.9%
317649
 
1.9%
Other values (2)33752
 
3.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown)933215
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0274454
29.4%
2222924
23.9%
1170893
18.3%
A58003
 
6.2%
B50940
 
5.5%
734239
 
3.7%
829990
 
3.2%
622471
 
2.4%
517900
 
1.9%
317649
 
1.9%
Other values (2)33752
 
3.6%
Distinct5023
Distinct (%)2.4%
Missing0
Missing (%)0.0%
Memory size10.1 MiB
2025-11-10T21:16:16.768666image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length6
Median length5
Mean length2.20781
Min length1

Characters and Unicode

Total characters454959
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2099 ?
Unique (%)1.0%

Sample

1st row2
2nd row82
3rd row2
4th row3
5th row1
ValueCountFrequency (%)
108153
 
4.0%
57741
 
3.8%
27037
 
3.4%
36391
 
3.1%
205954
 
2.9%
45911
 
2.9%
15583
 
2.7%
155175
 
2.5%
64835
 
2.3%
304756
 
2.3%
Other values (5013)144532
70.1%
2025-11-10T21:16:17.212906image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
086153
18.9%
176883
16.9%
257037
12.5%
552521
11.5%
341061
9.0%
434010
 
7.5%
626725
 
5.9%
826404
 
5.8%
723650
 
5.2%
917963
 
3.9%

Most occurring categories

ValueCountFrequency (%)
(unknown)454959
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
086153
18.9%
176883
16.9%
257037
12.5%
552521
11.5%
341061
9.0%
434010
 
7.5%
626725
 
5.9%
826404
 
5.8%
723650
 
5.2%
917963
 
3.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown)454959
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
086153
18.9%
176883
16.9%
257037
12.5%
552521
11.5%
341061
9.0%
434010
 
7.5%
626725
 
5.9%
826404
 
5.8%
723650
 
5.2%
917963
 
3.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown)454959
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
086153
18.9%
176883
16.9%
257037
12.5%
552521
11.5%
341061
9.0%
434010
 
7.5%
626725
 
5.9%
826404
 
5.8%
723650
 
5.2%
917963
 
3.9%
Distinct4557
Distinct (%)2.2%
Missing0
Missing (%)0.0%
Memory size10.0 MiB
2025-11-10T21:16:17.492326image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length6
Median length5
Mean length2.1178009
Min length1

Characters and Unicode

Total characters436411
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1879 ?
Unique (%)0.9%

Sample

1st row1
2nd row80
3rd row2
4th row3
5th row1
ValueCountFrequency (%)
28151
 
4.0%
58127
 
3.9%
107930
 
3.8%
37223
 
3.5%
16649
 
3.2%
46482
 
3.1%
205538
 
2.7%
84993
 
2.4%
154899
 
2.4%
64871
 
2.4%
Other values (4547)141205
68.5%
2025-11-10T21:16:17.895836image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
082995
19.0%
174022
17.0%
254333
12.4%
549545
11.4%
339994
9.2%
433264
7.6%
826200
 
6.0%
624956
 
5.7%
722482
 
5.2%
917732
 
4.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)436411
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
082995
19.0%
174022
17.0%
254333
12.4%
549545
11.4%
339994
9.2%
433264
7.6%
826200
 
6.0%
624956
 
5.7%
722482
 
5.2%
917732
 
4.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)436411
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
082995
19.0%
174022
17.0%
254333
12.4%
549545
11.4%
339994
9.2%
433264
7.6%
826200
 
6.0%
624956
 
5.7%
722482
 
5.2%
917732
 
4.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)436411
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
082995
19.0%
174022
17.0%
254333
12.4%
549545
11.4%
339994
9.2%
433264
7.6%
826200
 
6.0%
624956
 
5.7%
722482
 
5.2%
917732
 
4.1%
Distinct10230
Distinct (%)5.0%
Missing0
Missing (%)0.0%
Memory size10.2 MiB
2025-11-10T21:16:18.149223image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length9
Median length7
Mean length2.8780014
Min length1

Characters and Unicode

Total characters593064
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique5136 ?
Unique (%)2.5%

Sample

1st row1
2nd row1,440
3rd row26
4th row48
5th row5
ValueCountFrequency (%)
03807
 
1.8%
602553
 
1.2%
202497
 
1.2%
302489
 
1.2%
82423
 
1.2%
52417
 
1.2%
102401
 
1.2%
22387
 
1.2%
62345
 
1.1%
42334
 
1.1%
Other values (10220)180415
87.6%
2025-11-10T21:16:18.513377image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0112579
19.0%
181594
13.8%
269507
11.7%
554067
9.1%
450438
8.5%
346886
7.9%
642543
 
7.2%
,40364
 
6.8%
839832
 
6.7%
729864
 
5.0%

Most occurring categories

ValueCountFrequency (%)
(unknown)593064
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0112579
19.0%
181594
13.8%
269507
11.7%
554067
9.1%
450438
8.5%
346886
7.9%
642543
 
7.2%
,40364
 
6.8%
839832
 
6.7%
729864
 
5.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown)593064
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0112579
19.0%
181594
13.8%
269507
11.7%
554067
9.1%
450438
8.5%
346886
7.9%
642543
 
7.2%
,40364
 
6.8%
839832
 
6.7%
729864
 
5.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown)593064
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0112579
19.0%
181594
13.8%
269507
11.7%
554067
9.1%
450438
8.5%
346886
7.9%
642543
 
7.2%
,40364
 
6.8%
839832
 
6.7%
729864
 
5.0%

Rendimiento (t/ha)
Real number (ℝ)

Missing 

Distinct3621
Distinct (%)1.8%
Missing3433
Missing (%)1.7%
Infinite0
Infinite (%)0.0%
Mean9.2388197
Minimum0.03
Maximum246
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.6 MiB
2025-11-10T21:16:18.601543image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0.03
5-th percentile0.6
Q11.5
median5
Q311.23
95-th percentile29
Maximum246
Range245.97
Interquartile range (IQR)9.73

Descriptive statistics

Standard deviation14.888659
Coefficient of variation (CV)1.6115326
Kurtosis41.738654
Mean9.2388197
Median Absolute Deviation (MAD)3.9
Skewness5.4796096
Sum1872108.2
Variance221.67217
MonotonicityNot monotonic
2025-11-10T21:16:18.719275image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
109445
 
4.6%
87643
 
3.7%
17499
 
3.6%
67402
 
3.6%
57374
 
3.6%
156762
 
3.3%
26433
 
3.1%
126149
 
3.0%
46025
 
2.9%
75865
 
2.8%
Other values (3611)132038
64.1%
ValueCountFrequency (%)
0.032
 
< 0.1%
0.053
 
< 0.1%
0.063
 
< 0.1%
0.074
 
< 0.1%
0.087
 
< 0.1%
0.095
 
< 0.1%
0.125
< 0.1%
0.117
 
< 0.1%
0.1210
 
< 0.1%
0.138
 
< 0.1%
ValueCountFrequency (%)
2466
 
< 0.1%
2402
 
< 0.1%
2109
< 0.1%
2061
 
< 0.1%
200.691
 
< 0.1%
2009
< 0.1%
196.671
 
< 0.1%
192.111
 
< 0.1%
19018
< 0.1%
187.53
 
< 0.1%

ESTADO FISICO PRODUCCION
Categorical

High correlation 

Distinct23
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size12.1 MiB
FRUTO FRESCO
59682 
GRANO SECO
57290 
HORTALIZA FRESCA
31742 
TUBERCULO FRESCO
21755 
PADDY VERDE
7416 
Other values (18)
28183 

Length

Max length22
Median length18
Mean length12.627109
Min length2

Characters and Unicode

Total characters2602043
Distinct characters26
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowFRUTO FRESCO
2nd rowFRUTO FRESCO
3rd rowFRUTO FRESCO
4th rowFRUTO FRESCO
5th rowFRUTO FRESCO

Common Values

ValueCountFrequency (%)
FRUTO FRESCO59682
29.0%
GRANO SECO57290
27.8%
HORTALIZA FRESCA31742
15.4%
TUBERCULO FRESCO21755
 
10.6%
PADDY VERDE7416
 
3.6%
CAFE VERDE EQUIVALENTE7263
 
3.5%
PANELA6669
 
3.2%
LEGUMINOSA FRESCA3919
 
1.9%
HOJA FRESCA1799
 
0.9%
ACEITE CRUDO1382
 
0.7%
Other values (13)7151
 
3.5%

Length

2025-11-10T21:16:18.828474image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
fresco81818
19.9%
fruto59682
14.5%
seco58613
14.2%
grano57290
13.9%
fresca37989
9.2%
hortaliza31742
 
7.7%
tuberculo21755
 
5.3%
verde14679
 
3.6%
paddy7416
 
1.8%
cafe7263
 
1.8%
Other values (20)33360
8.1%

Most occurring characters

ValueCountFrequency (%)
O321252
12.3%
R308985
11.9%
E278725
10.7%
A213773
8.2%
C213185
8.2%
205539
7.9%
F188669
7.3%
S186018
7.1%
T123147
 
4.7%
U115756
 
4.4%
Other values (16)446994
17.2%

Most occurring categories

ValueCountFrequency (%)
(unknown)2602043
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
O321252
12.3%
R308985
11.9%
E278725
10.7%
A213773
8.2%
C213185
8.2%
205539
7.9%
F188669
7.3%
S186018
7.1%
T123147
 
4.7%
U115756
 
4.4%
Other values (16)446994
17.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown)2602043
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
O321252
12.3%
R308985
11.9%
E278725
10.7%
A213773
8.2%
C213185
8.2%
205539
7.9%
F188669
7.3%
S186018
7.1%
T123147
 
4.7%
U115756
 
4.4%
Other values (16)446994
17.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown)2602043
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
O321252
12.3%
R308985
11.9%
E278725
10.7%
A213773
8.2%
C213185
8.2%
205539
7.9%
F188669
7.3%
S186018
7.1%
T123147
 
4.7%
U115756
 
4.4%
Other values (16)446994
17.2%

NOMBRE CIENTIFICO
Text

Missing 

Distinct214
Distinct (%)0.1%
Missing2857
Missing (%)1.4%
Memory size12.6 MiB
2025-11-10T21:16:19.008242image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length47
Median length35
Mean length15.799283
Min length8

Characters and Unicode

Total characters3210588
Distinct characters32
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique9 ?
Unique (%)< 0.1%

Sample

1st rowBETA VULGARIS
2nd rowBETA VULGARIS
3rd rowBETA VULGARIS
4th rowBETA VULGARIS
5th rowBETA VULGARIS
ValueCountFrequency (%)
zea25199
 
5.9%
mays25199
 
5.9%
vulgaris21125
 
4.9%
phaseolus14693
 
3.4%
musa11613
 
2.7%
paradisiaca11137
 
2.6%
solanum10914
 
2.6%
esculenta10122
 
2.4%
lycopersicum9654
 
2.3%
esculetum9654
 
2.3%
Other values (350)277665
65.0%
2025-11-10T21:16:19.279035image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
A466875
14.5%
S300695
 
9.4%
U254090
 
7.9%
I246919
 
7.7%
223764
 
7.0%
E191539
 
6.0%
C186345
 
5.8%
M180400
 
5.6%
R168955
 
5.3%
L147153
 
4.6%
Other values (22)843853
26.3%

Most occurring categories

ValueCountFrequency (%)
(unknown)3210588
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
A466875
14.5%
S300695
 
9.4%
U254090
 
7.9%
I246919
 
7.7%
223764
 
7.0%
E191539
 
6.0%
C186345
 
5.8%
M180400
 
5.6%
R168955
 
5.3%
L147153
 
4.6%
Other values (22)843853
26.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown)3210588
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
A466875
14.5%
S300695
 
9.4%
U254090
 
7.9%
I246919
 
7.7%
223764
 
7.0%
E191539
 
6.0%
C186345
 
5.8%
M180400
 
5.6%
R168955
 
5.3%
L147153
 
4.6%
Other values (22)843853
26.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown)3210588
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
A466875
14.5%
S300695
 
9.4%
U254090
 
7.9%
I246919
 
7.7%
223764
 
7.0%
E191539
 
6.0%
C186345
 
5.8%
M180400
 
5.6%
R168955
 
5.3%
L147153
 
4.6%
Other values (22)843853
26.3%

CICLO DE CULTIVO
Categorical

High correlation 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size11.6 MiB
TRANSITORIO
108943 
PERMANENTE
82643 
ANUAL
14482 

Length

Max length11
Median length11
Mean length10.177286
Min length5

Characters and Unicode

Total characters2097213
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowTRANSITORIO
2nd rowTRANSITORIO
3rd rowTRANSITORIO
4th rowTRANSITORIO
5th rowTRANSITORIO

Common Values

ValueCountFrequency (%)
TRANSITORIO108943
52.9%
PERMANENTE82643
40.1%
ANUAL14482
 
7.0%

Length

2025-11-10T21:16:19.358437image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-10T21:16:19.428671image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
transitorio108943
52.9%
permanente82643
40.1%
anual14482
 
7.0%

Most occurring characters

ValueCountFrequency (%)
T300529
14.3%
R300529
14.3%
N288711
13.8%
E247929
11.8%
A220550
10.5%
I217886
10.4%
O217886
10.4%
S108943
 
5.2%
P82643
 
3.9%
M82643
 
3.9%
Other values (2)28964
 
1.4%

Most occurring categories

ValueCountFrequency (%)
(unknown)2097213
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
T300529
14.3%
R300529
14.3%
N288711
13.8%
E247929
11.8%
A220550
10.5%
I217886
10.4%
O217886
10.4%
S108943
 
5.2%
P82643
 
3.9%
M82643
 
3.9%
Other values (2)28964
 
1.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown)2097213
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
T300529
14.3%
R300529
14.3%
N288711
13.8%
E247929
11.8%
A220550
10.5%
I217886
10.4%
O217886
10.4%
S108943
 
5.2%
P82643
 
3.9%
M82643
 
3.9%
Other values (2)28964
 
1.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown)2097213
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
T300529
14.3%
R300529
14.3%
N288711
13.8%
E247929
11.8%
A220550
10.5%
I217886
10.4%
O217886
10.4%
S108943
 
5.2%
P82643
 
3.9%
M82643
 
3.9%
Other values (2)28964
 
1.4%

Interactions

2025-11-10T21:16:11.491515image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-10T21:16:11.187038image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-10T21:16:11.628935image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-10T21:16:11.346564image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Correlations

2025-11-10T21:16:19.485914image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
AÑOCICLO DE CULTIVOCÓD. DEP.DEPARTAMENTOESTADO FISICO PRODUCCIONGRUPO DE CULTIVOPERIODORendimiento (t/ha)
AÑO1.0000.1110.0170.0240.0430.0441.0000.013
CICLO DE CULTIVO0.1111.0000.0720.1600.8250.7700.7070.057
CÓD. \nDEP.0.0170.0721.0001.0000.0990.0770.0330.059
DEPARTAMENTO0.0240.1601.0001.0000.1150.1280.0330.064
ESTADO FISICO PRODUCCION0.0430.8250.0990.1151.0000.8830.1800.217
GRUPO \nDE CULTIVO0.0440.7700.0770.1280.8831.0000.2570.114
PERIODO1.0000.7070.0330.0330.1800.2571.0000.029
Rendimiento\n(t/ha)0.0130.0570.0590.0640.2170.1140.0291.000

Missing values

2025-11-10T21:16:11.904477image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2025-11-10T21:16:12.382447image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2025-11-10T21:16:13.103976image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

CÓD. DEP.DEPARTAMENTOCÓD. MUN.MUNICIPIOGRUPO DE CULTIVOSUBGRUPO DE CULTIVOCULTIVODESAGREGACIÓN REGIONAL Y/O SISTEMA PRODUCTIVOAÑOPERIODOÁrea Sembrada (ha)Área Cosechada (ha)Producción (t)Rendimiento (t/ha)ESTADO FISICO PRODUCCIONNOMBRE CIENTIFICOCICLO DE CULTIVO
015BOYACA15,114BUSBANZAHORTALIZASACELGAACELGAACELGA2,0062006B2111.00FRUTO FRESCOBETA VULGARISTRANSITORIO
125CUNDINAMARCA25,754SOACHAHORTALIZASACELGAACELGAACELGA2,0062006B82801,44018.00FRUTO FRESCOBETA VULGARISTRANSITORIO
225CUNDINAMARCA25,214COTAHORTALIZASACELGAACELGAACELGA2,0062006B222617.33FRUTO FRESCOBETA VULGARISTRANSITORIO
354NORTE DE SANTANDER54,405LOS PATIOSHORTALIZASACELGAACELGAACELGA2,0062006B334816.00FRUTO FRESCOBETA VULGARISTRANSITORIO
454NORTE DE SANTANDER54,518PAMPLONAHORTALIZASACELGAACELGAACELGA2,0062006B11510.00FRUTO FRESCOBETA VULGARISTRANSITORIO
568SANTANDER68,377LA BELLEZAHORTALIZASACELGAACELGAACELGA2,0062006B1166.00FRUTO FRESCOBETA VULGARISTRANSITORIO
625CUNDINAMARCA25,754SOACHAHORTALIZASACELGAACELGAACELGA2,0072007A72701,26018.00FRUTO FRESCOBETA VULGARISTRANSITORIO
725CUNDINAMARCA25,214COTAHORTALIZASACELGAACELGAACELGA2,0072007A223417.00FRUTO FRESCOBETA VULGARISTRANSITORIO
854NORTE DE SANTANDER54,518PAMPLONAHORTALIZASACELGAACELGAACELGA2,0072007A11510.00FRUTO FRESCOBETA VULGARISTRANSITORIO
968SANTANDER68,377LA BELLEZAHORTALIZASACELGAACELGAACELGA2,0072007A1166.00FRUTO FRESCOBETA VULGARISTRANSITORIO
CÓD. DEP.DEPARTAMENTOCÓD. MUN.MUNICIPIOGRUPO DE CULTIVOSUBGRUPO DE CULTIVOCULTIVODESAGREGACIÓN REGIONAL Y/O SISTEMA PRODUCTIVOAÑOPERIODOÁrea Sembrada (ha)Área Cosechada (ha)Producción (t)Rendimiento (t/ha)ESTADO FISICO PRODUCCIONNOMBRE CIENTIFICOCICLO DE CULTIVO
2060588ATLANTICO8,520PALMAR DE VARELALEGUMINOSASFRIJOLFRIJOLZARAGOZA2,0152015A5482.00GRANO SECOPHASEOLUS VULGARISTRANSITORIO
2060598ATLANTICO8,549PIOJOLEGUMINOSASFRIJOLFRIJOLZARAGOZA2,0152015A3100.30GRANO SECOPHASEOLUS VULGARISTRANSITORIO
20606025CUNDINAMARCA25,436MANTAHORTALIZASCALABACINCALABACINZUCCHINI2,0172017A201939921.00HORTALIZA FRESCACUCURBITA PEPOTRANSITORIO
20606125CUNDINAMARCA25,807TIBIRITAHORTALIZASCALABACINCALABACINZUCCHINI2,0172017A55408.00HORTALIZA FRESCACUCURBITA PEPOTRANSITORIO
20606225CUNDINAMARCA25,524PANDIHORTALIZASCALABACINCALABACINZUCCHINI2,0172017A33155.00HORTALIZA FRESCACUCURBITA PEPOTRANSITORIO
20606325CUNDINAMARCA25,436MANTAHORTALIZASCALABACINCALABACINZUCCHINI2,0172017B201818010.00HORTALIZA FRESCACUCURBITA PEPOTRANSITORIO
20606425CUNDINAMARCA25,524PANDIHORTALIZASCALABACINCALABACINZUCCHINI2,0172017B2285.00HORTALIZA FRESCACUCURBITA PEPOTRANSITORIO
20606525CUNDINAMARCA25,436MANTAHORTALIZASCALABACINCALABACINZUCCHINI2,0182018A151515010.00HORTALIZA FRESCACUCURBITA PEPOTRANSITORIO
20606625CUNDINAMARCA25,807TIBIRITAHORTALIZASCALABACINCALABACINZUCCHINI2,0182018A66508.27HORTALIZA FRESCACUCURBITA PEPOTRANSITORIO
20606725CUNDINAMARCA25,524PANDIHORTALIZASCALABACINCALABACINZUCCHINI2,0182018A55255.00HORTALIZA FRESCACUCURBITA PEPOTRANSITORIO